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Commonsense reasoning is fundamental to natural language understanding.
Wordnet: a lexical database for english
George A Miller. 1995 · 1995
Earlier work this paper cites.
Using the framework
Robin Cooper, Dick Crouch, Jan Van Eijck, Chris Fox, Johan Van Genabith, Jan Jaspars, Hans Kamp, David Milward, Manfred Pinkal, Massimo Poesio, and Steve Pulman. 1996 · 1996
Earlier work this paper cites.
A machine learning approach to coreference resolution of noun phrases
Wee Meng Soon, Hwee Tou Ng, and Daniel Chung Yong Lim. 2001 · 2001
Earlier work this paper cites.
Improving machine learning approaches to coreference resolution
Vincent Ng and Claire Cardie. 2002 · 2002
Earlier work this paper cites.
Conceptnet—a practical commonsense reasoning tool-kit
Hugo Liu and Push Singh. 2004 · 2004
Earlier work this paper cites.
The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
Earlier work this paper cites.
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Hector J Levesque, Ernest Davis, and Leora Morgenstern. 2011 · 2011
Earlier work this paper cites.
Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon. 2011 · 2011
Earlier work this paper cites.
Resolving complex cases of definite pronouns: The winograd schema challenge
Altaf Rahman and Vincent Ng. 2012 · 2012
Earlier work this paper cites.
Learning deep structured semantic models for web search using clickthrough data
Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013 · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Peter Schüller. 2014 · 2014
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Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
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Haoruo Peng, Daniel Khashabi, and Dan Roth. 2015 · 2015
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Haoruo Peng, Yangqiu Song, and Dan Roth. 2016 · 2016
Later among the works it cites.
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Later among the works it cites.
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Sheng Zhang, Rachel Rudinger, Kevin Duh, and Benjamin Van Durme. 2017 · 2017
Later among the works it cites.
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Jianfeng Gao, Michel Galley, and Lihong Li. 2018 · 2018
Later among the works it cites.
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Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
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Cited alongside, same era.
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Deep reinforcement learning for mention-ranking coreference models
Kevin Clark and Christopher D Manning. 2016a
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Improving coreference resolution by learning entity-level distributed representations
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Swag: A large-scale adversarial dataset for grounded commonsense inference
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ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
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Language models are unsupervised multitask learners
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